The healthcare industry, among various modern day industries, may produce data at a staggering rate. Managing this data and drawing meaningful conclusions and insights may be critical for the operational success of organizations of this industry. For instance, the healthcare industry may maintain various types of records of human subjects/patients such as, but not limited to, medical diagnosis records, medical insurance records, hospital data, etc. The records of the human subjects/patients may be analyzed using various mathematical models to identify trends and categorize the data into different risk profiles (e.g., risk of contacting a disease, life expectancy, and health insurance risk profile).
Typically, the data, which is to be analyzed, may include fields of various types. For example, medical records may include various fields of numerical data type, for instance, BP measure, heart rate, and blood sugar measure. Further, the medical records may also include various fields of categorical data type, for example, gender. Categorization of records of such varied types may be cumbersome as a mathematical model suited to categorize data of one type may not work well with data of another type. Thus, categorization of mixed data (i.e., numerical and categorical) may be difficult. Further, analysis of records of a large number of fields exacerbates the already difficult task.